Line Variance Data

get_arbitrage_opportunities

Scans every surveyed market for riskless cross-book arbitrage (the combined implied margin of the single best price per side is strictly < 1.0) and returns exact proportional stake distributions, clamped to the 4.0-unit institutional cap. Covers the five professional leagues (mlb, nfl, nba, wnba, nhl). Omit sport for the all-league aggregate. Collegiate and NCAA rows are never returned. Kalshi legs render as cents plus American odds (42¢ (+138)); sportsbook legs stay American (-110, +125).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_arbitrage_opportunities"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Maximum number of results to return.
`sport`	No	Optional professional-league filter. Case-insensitive. Allowed values: mlb, nfl, nba, wnba, nhl. Omit to return the aggregate across all five active pro leagues. Collegiate and NCAA rows are never returned.
`league`	No	Legacy alias for sport. Case-insensitive professional league: mlb, nfl, nba, wnba, nhl. Ignored when sport is also set. Omit both sport and league for the all-league aggregate.
`market_name`	No	Optional exact market name filter, e.g. 'Moneyline'.
`stake_units`	No	Total stake units to distribute (clamped to 4.0).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 9, 2026

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description carries real weight: it defines the arbitrage threshold, discloses the 4.0-unit stake cap, enumerates the five eligible leagues, and specifies the exact output rendering for Kalshi legs (cents + American odds) versus sportsbook legs (American only). It omits ordering/pagination behavior and any auth or rate-limit notes, which keeps it just below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense sentences, front-loaded with the detection criterion and followed by scope, exclusions, and output format. Every sentence carries distinct information, though the sentences run long and the output-format detail could be split for faster scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless-required scan tool with no output schema, the description covers scope, exclusion rules, stake clamping, and leg-rendering formats—enough to call it correctly. Missing are result ordering and how limit interacts with the scan, a minor but real gap given the 5-parameter surface.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so limit, sport, league, market_name, and stake_units are all documented in the schema, including the clamping and the league-as-alias rule. The description restates the 4.0 clamp and the sport-omission rule but adds no syntax or format details beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('scans every surveyed market for riskless cross-book arbitrage') plus the precise detection criterion (best price per side strictly < 1.0) and what it returns (proportional stake distributions). An agent can instantly distinguish this from get_line_variance and get_sharp_consensus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear operational context: omit sport for the all-league aggregate, collegiate/NCAA rows are never returned. However, it never explicitly names the sibling tools (get_line_variance, get_sharp_consensus) or states the conditions that should route an agent to those instead, so the alternative-selection guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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